Introduction to Linear Algebra by Gilbert Strang for Machine Learning - Machine Learning Mastery

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Concepts in the book are laid out clearly, often with diagrams, but the book moves quickly. The book expects you to keep up or you will fall behind. That being said, each section has an overview of the concepts to be covered and ends with worked examples and quiz questions, the answers to which are available on the book's website. Take my free 7-day email crash course now (with sample code). Click to sign-up and also get a free PDF Ebook version of the course.


Key Machine Learning PreReq: Viewing Linear Algebra through the right lenses

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Think Sets and Functions, rather than manipulation of number arrays/rectangles: Linear Algebra is often introduced at the high-school level as computations one can perform on vectors and matrices - Matrix multiplication, Gauss elimination, Determinants, sometimes even Eigenvalue calculations, and I believe this introduction is quite detrimental to one's understanding of Linear Algebra. This computational approach continues on in many undergrad (and sometimes grad) level courses in Engineering and the Social Sciences. In fact, many Computer Scientists deal with Linear Algebra for decades of their professional life with this narrow (and in my opinion, harmful) view. I believe the right way to learn Linear Algebra is to view vectors as elements in a Set (Vector Space), and matrices as functions from one vector space to another. A vector of n numbers is an element in the vector space R n, and a m x n matrix is a function from R n to R m.


Boost your data science skills. Learn linear algebra.

@machinelearnbot

Graphical representation is also very helpful to understand linear algebra. I tried to bind the concepts with plots (and code to produce it). The type of representation I liked most by doing this series is the fact that you can see any matrix as linear transformation of the space. In several chapters we will extend this idea and see how it can be useful to understand eigendecomposition, Singular Value Decomposition (SVD) or the Principal Components Analysis (PCA). In addition, I noticed that creating and reading examples is really helpful to understand the theory. It is why I built Python notebooks.


Deep Learning Book Series · Introduction

#artificialintelligence

Graphical representation is also very helpful to understand linear algebra. I tried to bind the concepts with plots (and code to produce it). The type of representation I liked most by doing this series is the fact that you can see any matrix as linear transformation of the space. In several chapters we will extend this idea and see how it can be useful to understand eigendecomposition, Singular Value Decomposition (SVD) or the Principal Components Analysis (PCA). In addition, I noticed that creating and reading examples is really helpful to understand the theory. It is why I built Python notebooks.


Boost your data science skills. Learn linear algebra.

#artificialintelligence

This chapter is mainly on the dot product (vector and/or matrix multiplication). We will also see some of its properties. Then, we will see how to synthesize a system of linear equations using matrix notation. This is a major process for the following chapters.